{"id":"W2109709592","doi":"10.1016/j.enggeo.2008.06.016","title":"CAT-scan analysis of sedimentary sequences: An ultrahigh-resolution paleoclimatic tool","year":2008,"lang":"en","type":"article","venue":"Engineering Geology","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Institut Polaire Français Paul Emile Victor; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Geology; Sedimentary rock; Paleoclimatology; Holocene; Context (archaeology); Climatology; Estuary; Spectral analysis; Climate change; North Atlantic oscillation; Oceanography; Paleontology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003315443,0.0001448673,0.0003951009,0.0004659652,0.0001245788,0.000003423637,0.0002571882,0.0001718604,0.002322974],"category_scores_gemma":[0.00006452181,0.0001343173,0.00009579085,0.000624575,0.0003085804,0.0001109415,0.00001142348,0.0001789367,0.0001153848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002384379,"about_ca_system_score_gemma":0.0000566124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001447204,"about_ca_topic_score_gemma":0.003528825,"domain_scores_codex":[0.9986722,0.0001338722,0.0003051451,0.0002742553,0.0001586015,0.0004558504],"domain_scores_gemma":[0.999182,0.0003257713,0.00006921287,0.0002777307,0.00004125385,0.0001039617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001714161,0.00001556663,0.7992619,0.00001782002,0.0002451562,0.00005438144,0.0002945334,0.1998727,0.00006589926,0.00009611963,0.00001894151,0.0000398478],"study_design_scores_gemma":[0.0001329397,0.0001520949,0.8034362,0.000002932717,0.0001151101,0.0001262695,0.00004106731,0.1957186,0.00005712431,0.00003982818,0.00006200173,0.0001158328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997961,0.0003512484,0.0005893392,0.0001113297,0.0002257518,0.00009456171,0.00002615549,0.00006259145,0.0005780646],"genre_scores_gemma":[0.9964743,0.00009596806,0.002686066,0.00006627211,0.00003736986,0.000003210992,0.0005460145,0.000002875677,0.00008788984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004174266,"threshold_uncertainty_score":0.998589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679075896404383,"score_gpt":0.2201509970431674,"score_spread":0.2033602380791235,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}